NumpyGaussianDistribution

class probly.representation.distribution.numpy_gaussian.NumpyGaussianDistribution(mean: ndarray, var: ndarray, type: Literal['gaussian'] = 'gaussian')

Bases: NumpyAxisProtected[ndarray], GaussianDistribution[ndarray]

Gaussian distribution with array parameters.

property T: Self

The transposed version of the underlying array.

allowed_types: ClassVar[tuple[type[np.ndarray] | type[np.generic] | type[float] | type[int], ...]] = (<class 'numpy.ndarray'>, <class 'numpy.generic'>, <class 'float'>, <class 'int'>)

astype(dtype: DTypeLike, order: Order = 'K', casting: Literal['no', 'equiv', 'safe', 'same_kind', 'unsafe'] = 'unsafe', subok: bool = True, copy: bool = True) → Self

Cast each protected field using ndarray.astype’s casting and layout options.

copy(order: Order = 'C') → Self

Return a copy of the array.

property device: str

Device of the array.

property dtype: dtype

Data type of the array.

entropy() → ArrayLike

Compute entropy.

property flags: ArrayFlagsLike

The flags of the array.

property mT: Self

The transposed version of the underlying array.

mean: np.ndarray

property ndim: int

Number of dimensions.

permitted_functions = {}

permitted_ufuncs = {}

classmethod primary_protected_name() → str

Return the first protected field (dict order).

protected_axes: ClassVar[dict[str, int]] = {'mean': 0, 'var': 0}

property protected_shape: tuple[int, ...]

Protected trailing shape of the primary field.

protected_value() → NumpyProtectedValue

Return the primary protected value.

protected_values(func: Callable | None = None, method: str | None = None) → dict[str, NumpyProtectedValue] | None

Return all protected field values.

The values are preserved as-is and are not coerced to np.ndarray. Optionally takes the function that triggered the call for context. This can be used to conditionally modify the returned values or prevent them from being accessed.

quantile(q: float | list[float] | ndarray) → ndarray

Calculate the quantile function at the given points.

reshape(*shape: int | tuple[int, ...], order: str = 'C', copy: bool | None = None) → Self

Return a copy with reshaped protected values.

sample(num_samples: int = 1, rng: Generator | None = None) → NumpySample[ndarray]

Draw samples and wrap them in an NumpySample (sample_axis=0).

property shape: tuple[int, ...]

Shape of the array.

property size: int

Number of elements in the array.

property std: ndarray

Get the standard deviation.

to_device(device: Any, /, *, stream: Any = None) → Self

Move the array to a device.

transpose(*axes: int | None) → Self

Return a transposed version of the NumpySample.

This method implicitly also provides full axis tracking support for - np.moveaxis - np.rollaxis Those functions call out to transpose methods for custom array types.

Parameters:

axes – The axes to transpose.

Returns:

A transposed version of the NumpySample.

type: Literal['gaussian']

var: np.ndarray

with_protected_values(values: dict[str, NumpyProtectedValue], func: Callable | None = None) → NumpyAxisProtected[T]

Return a copy with updated protected field values.